Overview

Dataset statistics

Number of variables16
Number of observations197428
Missing cells56061
Missing cells (%)1.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.1 MiB
Average record size in memory128.0 B

Variable types

Numeric13
Categorical3

Alerts

created_at has a high cardinality: 180985 distinct valuesHigh cardinality
actual_delivery_time has a high cardinality: 178110 distinct valuesHigh cardinality
store_primary_category has a high cardinality: 74 distinct valuesHigh cardinality
order_protocol is highly overall correlated with estimated_order_place_durationHigh correlation
total_items is highly overall correlated with subtotal and 2 other fieldsHigh correlation
subtotal is highly overall correlated with total_items and 2 other fieldsHigh correlation
num_distinct_items is highly overall correlated with total_items and 2 other fieldsHigh correlation
min_item_price is highly overall correlated with total_items and 1 other fieldsHigh correlation
max_item_price is highly overall correlated with subtotalHigh correlation
total_onshift_dashers is highly overall correlated with total_busy_dashers and 1 other fieldsHigh correlation
total_busy_dashers is highly overall correlated with total_onshift_dashers and 1 other fieldsHigh correlation
total_outstanding_orders is highly overall correlated with total_onshift_dashers and 1 other fieldsHigh correlation
estimated_order_place_duration is highly overall correlated with order_protocolHigh correlation
store_primary_category has 4760 (2.4%) missing valuesMissing
total_onshift_dashers has 16262 (8.2%) missing valuesMissing
total_busy_dashers has 16262 (8.2%) missing valuesMissing
total_outstanding_orders has 16262 (8.2%) missing valuesMissing
total_items is highly skewed (γ1 = 21.41382417)Skewed
created_at is uniformly distributedUniform
actual_delivery_time is uniformly distributedUniform
min_item_price has 2539 (1.3%) zerosZeros
total_onshift_dashers has 3615 (1.8%) zerosZeros
total_busy_dashers has 4171 (2.1%) zerosZeros
total_outstanding_orders has 4111 (2.1%) zerosZeros

Reproduction

Analysis started2022-12-28 08:23:16.579813
Analysis finished2022-12-28 08:24:01.124950
Duration44.55 seconds
Software versionpandas-profiling vv3.6.1
Download configurationconfig.json

Variables

market_id
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing987
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.9787061
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-12-28T13:54:01.213523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile6
Maximum6
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5248667
Coefficient of variation (CV)0.51192252
Kurtosis-0.93810124
Mean2.9787061
Median Absolute Deviation (MAD)1
Skewness0.36120013
Sum585140
Variance2.3252185
MonotonicityNot monotonic
2022-12-28T13:54:01.299244image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 55058
27.9%
4 47599
24.1%
1 38037
19.3%
3 23297
11.8%
5 18000
 
9.1%
6 14450
 
7.3%
(Missing) 987
 
0.5%
ValueCountFrequency (%)
1 38037
19.3%
2 55058
27.9%
3 23297
11.8%
4 47599
24.1%
5 18000
 
9.1%
6 14450
 
7.3%
ValueCountFrequency (%)
6 14450
 
7.3%
5 18000
 
9.1%
4 47599
24.1%
3 23297
11.8%
2 55058
27.9%
1 38037
19.3%

created_at
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct180985
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2015-02-11 19:50:43
 
6
2015-01-24 01:56:33
 
6
2015-02-16 01:48:11
 
5
2015-02-08 02:20:03
 
5
2015-02-11 19:51:06
 
5
Other values (180980)
197401 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters3751132
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique165374 ?
Unique (%)83.8%

Sample

1st row2015-02-06 22:24:17
2nd row2015-02-10 21:49:25
3rd row2015-01-22 20:39:28
4th row2015-02-03 21:21:45
5th row2015-02-15 02:40:36

Common Values

ValueCountFrequency (%)
2015-02-11 19:50:43 6
 
< 0.1%
2015-01-24 01:56:33 6
 
< 0.1%
2015-02-16 01:48:11 5
 
< 0.1%
2015-02-08 02:20:03 5
 
< 0.1%
2015-02-11 19:51:06 5
 
< 0.1%
2015-02-11 19:50:41 5
 
< 0.1%
2015-01-31 01:41:10 5
 
< 0.1%
2015-01-31 02:16:03 4
 
< 0.1%
2015-02-16 01:47:34 4
 
< 0.1%
2015-02-03 02:04:13 4
 
< 0.1%
Other values (180975) 197379
> 99.9%

Length

2022-12-28T13:54:01.396686image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015-02-07 9149
 
2.3%
2015-02-15 9087
 
2.3%
2015-02-14 9016
 
2.3%
2015-02-08 8874
 
2.2%
2015-01-24 8230
 
2.1%
2015-01-31 8146
 
2.1%
2015-01-25 7934
 
2.0%
2015-02-16 7932
 
2.0%
2015-02-01 7724
 
2.0%
2015-02-13 7383
 
1.9%
Other values (46097) 311381
78.9%

Most occurring characters

ValueCountFrequency (%)
0 733984
19.6%
2 595423
15.9%
1 523163
13.9%
- 394856
10.5%
: 394856
10.5%
5 334468
8.9%
197428
 
5.3%
3 175161
 
4.7%
4 144084
 
3.8%
9 66680
 
1.8%
Other values (3) 191029
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2763992
73.7%
Dash Punctuation 394856
 
10.5%
Other Punctuation 394856
 
10.5%
Space Separator 197428
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 733984
26.6%
2 595423
21.5%
1 523163
18.9%
5 334468
12.1%
3 175161
 
6.3%
4 144084
 
5.2%
9 66680
 
2.4%
7 64293
 
2.3%
6 63494
 
2.3%
8 63242
 
2.3%
Dash Punctuation
ValueCountFrequency (%)
- 394856
100.0%
Other Punctuation
ValueCountFrequency (%)
: 394856
100.0%
Space Separator
ValueCountFrequency (%)
197428
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3751132
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 733984
19.6%
2 595423
15.9%
1 523163
13.9%
- 394856
10.5%
: 394856
10.5%
5 334468
8.9%
197428
 
5.3%
3 175161
 
4.7%
4 144084
 
3.8%
9 66680
 
1.8%
Other values (3) 191029
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3751132
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 733984
19.6%
2 595423
15.9%
1 523163
13.9%
- 394856
10.5%
: 394856
10.5%
5 334468
8.9%
197428
 
5.3%
3 175161
 
4.7%
4 144084
 
3.8%
9 66680
 
1.8%
Other values (3) 191029
 
5.1%

actual_delivery_time
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct178110
Distinct (%)90.2%
Missing7
Missing (%)< 0.1%
Memory size1.5 MiB
2015-02-11 20:40:45
 
5
2015-02-16 03:51:49
 
5
2015-02-01 03:44:13
 
5
2015-01-24 03:41:03
 
5
2015-02-12 03:14:14
 
5
Other values (178105)
197396 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters3750999
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160456 ?
Unique (%)81.3%

Sample

1st row2015-02-06 23:27:16
2nd row2015-02-10 22:56:29
3rd row2015-01-22 21:09:09
4th row2015-02-03 22:13:00
5th row2015-02-15 03:20:26

Common Values

ValueCountFrequency (%)
2015-02-11 20:40:45 5
 
< 0.1%
2015-02-16 03:51:49 5
 
< 0.1%
2015-02-01 03:44:13 5
 
< 0.1%
2015-01-24 03:41:03 5
 
< 0.1%
2015-02-12 03:14:14 5
 
< 0.1%
2015-02-05 03:10:31 5
 
< 0.1%
2015-02-08 04:09:25 5
 
< 0.1%
2015-02-14 03:21:32 4
 
< 0.1%
2015-02-06 03:04:54 4
 
< 0.1%
2015-02-11 03:28:30 4
 
< 0.1%
Other values (178100) 197374
> 99.9%
(Missing) 7
 
< 0.1%

Length

2022-12-28T13:54:01.493195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2015-02-07 9079
 
2.3%
2015-02-15 9011
 
2.3%
2015-02-14 8916
 
2.3%
2015-02-08 8898
 
2.3%
2015-01-24 8149
 
2.1%
2015-02-16 8147
 
2.1%
2015-01-31 8107
 
2.1%
2015-01-25 7774
 
2.0%
2015-02-01 7651
 
1.9%
2015-02-13 7362
 
1.9%
Other values (46108) 311748
79.0%

Most occurring characters

ValueCountFrequency (%)
0 737718
19.7%
2 591865
15.8%
1 502637
13.4%
- 394842
10.5%
: 394842
10.5%
5 340425
9.1%
197421
 
5.3%
3 183671
 
4.9%
4 156275
 
4.2%
6 66442
 
1.8%
Other values (3) 184861
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2763894
73.7%
Dash Punctuation 394842
 
10.5%
Other Punctuation 394842
 
10.5%
Space Separator 197421
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 737718
26.7%
2 591865
21.4%
1 502637
18.2%
5 340425
12.3%
3 183671
 
6.6%
4 156275
 
5.7%
6 66442
 
2.4%
7 63787
 
2.3%
8 61786
 
2.2%
9 59288
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 394842
100.0%
Other Punctuation
ValueCountFrequency (%)
: 394842
100.0%
Space Separator
ValueCountFrequency (%)
197421
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3750999
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 737718
19.7%
2 591865
15.8%
1 502637
13.4%
- 394842
10.5%
: 394842
10.5%
5 340425
9.1%
197421
 
5.3%
3 183671
 
4.9%
4 156275
 
4.2%
6 66442
 
1.8%
Other values (3) 184861
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3750999
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 737718
19.7%
2 591865
15.8%
1 502637
13.4%
- 394842
10.5%
: 394842
10.5%
5 340425
9.1%
197421
 
5.3%
3 183671
 
4.9%
4 156275
 
4.2%
6 66442
 
1.8%
Other values (3) 184861
 
4.9%

store_id
Real number (ℝ)

Distinct6743
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3530.5103
Minimum1
Maximum6987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-12-28T13:54:01.605592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile375
Q11686
median3592
Q35299
95-th percentile6709
Maximum6987
Range6986
Interquartile range (IQR)3613

Descriptive statistics

Standard deviation2053.4967
Coefficient of variation (CV)0.58164304
Kurtosis-1.2421394
Mean3530.5103
Median Absolute Deviation (MAD)1813
Skewness-0.0068077416
Sum6.9702158 × 108
Variance4216848.7
MonotonicityNot monotonic
2022-12-28T13:54:02.084315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6865 937
 
0.5%
1311 863
 
0.4%
314 815
 
0.4%
1686 765
 
0.4%
3937 721
 
0.4%
6917 668
 
0.3%
6074 632
 
0.3%
4367 593
 
0.3%
3748 560
 
0.3%
6503 548
 
0.3%
Other values (6733) 190326
96.4%
ValueCountFrequency (%)
1 8
 
< 0.1%
2 5
 
< 0.1%
3 3
 
< 0.1%
4 136
0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 19
 
< 0.1%
8 1
 
< 0.1%
9 12
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
6987 2
 
< 0.1%
6986 39
< 0.1%
6985 2
 
< 0.1%
6984 4
 
< 0.1%
6983 24
< 0.1%
6982 18
< 0.1%
6981 25
< 0.1%
6980 5
 
< 0.1%
6979 5
 
< 0.1%
6977 39
< 0.1%

store_primary_category
Categorical

HIGH CARDINALITY  MISSING 

Distinct74
Distinct (%)< 0.1%
Missing4760
Missing (%)2.4%
Memory size1.5 MiB
american
19399 
pizza
17321 
mexican
17099 
burger
 
10958
sandwich
 
10060
Other values (69)
117831 

Length

Max length17
Median length14
Mean length6.9550989
Min length4

Characters and Unicode

Total characters1340025
Distinct characters26
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowamerican
2nd rowmexican
3rd rowindian
4th rowitalian
5th rowitalian

Common Values

ValueCountFrequency (%)
american 19399
 
9.8%
pizza 17321
 
8.8%
mexican 17099
 
8.7%
burger 10958
 
5.6%
sandwich 10060
 
5.1%
chinese 9421
 
4.8%
japanese 9196
 
4.7%
dessert 8773
 
4.4%
fast 7372
 
3.7%
indian 7314
 
3.7%
Other values (64) 75755
38.4%

Length

2022-12-28T13:54:02.222387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
american 19399
 
10.1%
pizza 17321
 
9.0%
mexican 17099
 
8.9%
burger 10958
 
5.7%
sandwich 10060
 
5.2%
chinese 9421
 
4.9%
japanese 9196
 
4.8%
dessert 8773
 
4.6%
fast 7372
 
3.8%
indian 7314
 
3.8%
Other values (64) 75755
39.3%

Most occurring characters

ValueCountFrequency (%)
a 208055
15.5%
e 184292
13.8%
i 140842
10.5%
n 120198
9.0%
s 87943
 
6.6%
r 87208
 
6.5%
c 66888
 
5.0%
t 61611
 
4.6%
m 55053
 
4.1%
d 42279
 
3.2%
Other values (16) 285656
21.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1335933
99.7%
Dash Punctuation 4092
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 208055
15.6%
e 184292
13.8%
i 140842
10.5%
n 120198
9.0%
s 87943
 
6.6%
r 87208
 
6.5%
c 66888
 
5.0%
t 61611
 
4.6%
m 55053
 
4.1%
d 42279
 
3.2%
Other values (15) 281564
21.1%
Dash Punctuation
ValueCountFrequency (%)
- 4092
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1335933
99.7%
Common 4092
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 208055
15.6%
e 184292
13.8%
i 140842
10.5%
n 120198
9.0%
s 87943
 
6.6%
r 87208
 
6.5%
c 66888
 
5.0%
t 61611
 
4.6%
m 55053
 
4.1%
d 42279
 
3.2%
Other values (15) 281564
21.1%
Common
ValueCountFrequency (%)
- 4092
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1340025
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 208055
15.5%
e 184292
13.8%
i 140842
10.5%
n 120198
9.0%
s 87943
 
6.6%
r 87208
 
6.5%
c 66888
 
5.0%
t 61611
 
4.6%
m 55053
 
4.1%
d 42279
 
3.2%
Other values (16) 285656
21.3%

order_protocol
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing995
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.8823517
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-12-28T13:54:02.316101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5037712
Coefficient of variation (CV)0.52171676
Kurtosis-1.3045907
Mean2.8823517
Median Absolute Deviation (MAD)2
Skewness0.13709259
Sum566189
Variance2.2613278
MonotonicityNot monotonic
2022-12-28T13:54:02.392231image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 54725
27.7%
3 53199
26.9%
5 44290
22.4%
2 24052
12.2%
4 19354
 
9.8%
6 794
 
0.4%
7 19
 
< 0.1%
(Missing) 995
 
0.5%
ValueCountFrequency (%)
1 54725
27.7%
2 24052
12.2%
3 53199
26.9%
4 19354
 
9.8%
5 44290
22.4%
6 794
 
0.4%
7 19
 
< 0.1%
ValueCountFrequency (%)
7 19
 
< 0.1%
6 794
 
0.4%
5 44290
22.4%
4 19354
 
9.8%
3 53199
26.9%
2 24052
12.2%
1 54725
27.7%

total_items
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct57
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1963906
Minimum1
Maximum411
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-12-28T13:54:02.514462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile7
Maximum411
Range410
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.6665461
Coefficient of variation (CV)0.83423662
Kurtosis2801.4213
Mean3.1963906
Median Absolute Deviation (MAD)1
Skewness21.413824
Sum631057
Variance7.1104679
MonotonicityNot monotonic
2022-12-28T13:54:02.648190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 55134
27.9%
1 40619
20.6%
3 39253
19.9%
4 25138
12.7%
5 14055
 
7.1%
6 8618
 
4.4%
7 5023
 
2.5%
8 3050
 
1.5%
9 1861
 
0.9%
10 1284
 
0.7%
Other values (47) 3393
 
1.7%
ValueCountFrequency (%)
1 40619
20.6%
2 55134
27.9%
3 39253
19.9%
4 25138
12.7%
5 14055
 
7.1%
6 8618
 
4.4%
7 5023
 
2.5%
8 3050
 
1.5%
9 1861
 
0.9%
10 1284
 
0.7%
ValueCountFrequency (%)
411 1
< 0.1%
84 1
< 0.1%
66 1
< 0.1%
64 1
< 0.1%
59 1
< 0.1%
57 1
< 0.1%
56 1
< 0.1%
51 1
< 0.1%
50 2
< 0.1%
49 2
< 0.1%

subtotal
Real number (ℝ)

Distinct8368
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2682.3314
Minimum0
Maximum27100
Zeros179
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-12-28T13:54:02.799898image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile805
Q11400
median2200
Q33395
95-th percentile6200
Maximum27100
Range27100
Interquartile range (IQR)1995

Descriptive statistics

Standard deviation1823.0937
Coefficient of variation (CV)0.67966758
Kurtosis5.9985775
Mean2682.3314
Median Absolute Deviation (MAD)906
Skewness1.9615013
Sum5.2956732 × 108
Variance3323670.6
MonotonicityNot monotonic
2022-12-28T13:54:02.938411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1500 961
 
0.5%
1700 913
 
0.5%
2000 904
 
0.5%
1200 887
 
0.4%
1800 883
 
0.4%
1600 880
 
0.4%
1095 877
 
0.4%
1300 871
 
0.4%
2200 864
 
0.4%
1400 852
 
0.4%
Other values (8358) 188536
95.5%
ValueCountFrequency (%)
0 179
0.1%
12 1
 
< 0.1%
95 1
 
< 0.1%
100 1
 
< 0.1%
109 1
 
< 0.1%
125 1
 
< 0.1%
139 1
 
< 0.1%
145 2
 
< 0.1%
149 3
 
< 0.1%
152 1
 
< 0.1%
ValueCountFrequency (%)
27100 1
< 0.1%
26800 1
< 0.1%
24300 1
< 0.1%
22500 1
< 0.1%
20350 1
< 0.1%
19650 1
< 0.1%
19250 1
< 0.1%
18920 1
< 0.1%
18370 1
< 0.1%
17810 1
< 0.1%

num_distinct_items
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6707914
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-12-28T13:54:03.063524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile6
Maximum20
Range19
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6302552
Coefficient of variation (CV)0.61040157
Kurtosis4.2696206
Mean2.6707914
Median Absolute Deviation (MAD)1
Skewness1.5914791
Sum527289
Variance2.6577322
MonotonicityNot monotonic
2022-12-28T13:54:03.165275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2 59174
30.0%
1 49839
25.2%
3 41843
21.2%
4 23185
 
11.7%
5 11772
 
6.0%
6 5696
 
2.9%
7 2917
 
1.5%
8 1419
 
0.7%
9 734
 
0.4%
10 389
 
0.2%
Other values (10) 460
 
0.2%
ValueCountFrequency (%)
1 49839
25.2%
2 59174
30.0%
3 41843
21.2%
4 23185
 
11.7%
5 11772
 
6.0%
6 5696
 
2.9%
7 2917
 
1.5%
8 1419
 
0.7%
9 734
 
0.4%
10 389
 
0.2%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 2
 
< 0.1%
18 3
 
< 0.1%
17 2
 
< 0.1%
16 6
 
< 0.1%
15 13
 
< 0.1%
14 35
 
< 0.1%
13 60
 
< 0.1%
12 105
0.1%
11 233
0.1%

min_item_price
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2312
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean686.21847
Minimum-86
Maximum14700
Zeros2539
Zeros (%)1.3%
Negative13
Negative (%)< 0.1%
Memory size1.5 MiB
2022-12-28T13:54:03.295915image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-86
5-th percentile125
Q1299
median595
Q3949
95-th percentile1595
Maximum14700
Range14786
Interquartile range (IQR)650

Descriptive statistics

Standard deviation522.03865
Coefficient of variation (CV)0.76074701
Kurtosis14.602635
Mean686.21847
Median Absolute Deviation (MAD)304
Skewness2.3312796
Sum1.3547874 × 108
Variance272524.35
MonotonicityNot monotonic
2022-12-28T13:54:03.438154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
795 4197
 
2.1%
250 4032
 
2.0%
150 3913
 
2.0%
200 3863
 
2.0%
350 3439
 
1.7%
500 3352
 
1.7%
300 3317
 
1.7%
600 3069
 
1.6%
695 3068
 
1.6%
495 2852
 
1.4%
Other values (2302) 162326
82.2%
ValueCountFrequency (%)
-86 1
< 0.1%
-52 1
< 0.1%
-51 1
< 0.1%
-48 1
< 0.1%
-35 1
< 0.1%
-31 1
< 0.1%
-30 1
< 0.1%
-13 2
< 0.1%
-9 1
< 0.1%
-7 1
< 0.1%
ValueCountFrequency (%)
14700 1
< 0.1%
8999 1
< 0.1%
8959 1
< 0.1%
8415 1
< 0.1%
7999 2
< 0.1%
7500 1
< 0.1%
7475 1
< 0.1%
7398 1
< 0.1%
7299 1
< 0.1%
7259 1
< 0.1%

max_item_price
Real number (ℝ)

Distinct2652
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1159.5886
Minimum0
Maximum14700
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-12-28T13:54:03.580438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile439
Q1800
median1095
Q31395
95-th percentile2099
Maximum14700
Range14700
Interquartile range (IQR)595

Descriptive statistics

Standard deviation558.41138
Coefficient of variation (CV)0.48155989
Kurtosis12.915451
Mean1159.5886
Median Absolute Deviation (MAD)296
Skewness2.201033
Sum2.2893526 × 108
Variance311823.27
MonotonicityNot monotonic
2022-12-28T13:54:03.712610image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1095 5648
 
2.9%
995 5057
 
2.6%
1195 4349
 
2.2%
1295 4214
 
2.1%
999 3858
 
2.0%
895 3749
 
1.9%
795 3606
 
1.8%
799 3405
 
1.7%
1200 3365
 
1.7%
1395 3136
 
1.6%
Other values (2642) 157041
79.5%
ValueCountFrequency (%)
0 7
 
< 0.1%
52 1
 
< 0.1%
60 1
 
< 0.1%
75 2
 
< 0.1%
85 1
 
< 0.1%
95 3
 
< 0.1%
99 20
 
< 0.1%
100 22
< 0.1%
109 51
< 0.1%
110 3
 
< 0.1%
ValueCountFrequency (%)
14700 1
 
< 0.1%
8999 1
 
< 0.1%
8959 1
 
< 0.1%
8500 1
 
< 0.1%
8415 1
 
< 0.1%
7999 3
< 0.1%
7950 1
 
< 0.1%
7900 1
 
< 0.1%
7699 2
< 0.1%
7513 1
 
< 0.1%

total_onshift_dashers
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct172
Distinct (%)0.1%
Missing16262
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean44.808093
Minimum-4
Maximum171
Zeros3615
Zeros (%)1.8%
Negative21
Negative (%)< 0.1%
Memory size1.5 MiB
2022-12-28T13:54:03.861511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-4
5-th percentile4
Q117
median37
Q365
95-th percentile115
Maximum171
Range175
Interquartile range (IQR)48

Descriptive statistics

Standard deviation34.526783
Coefficient of variation (CV)0.77054793
Kurtosis-0.035222417
Mean44.808093
Median Absolute Deviation (MAD)23
Skewness0.8607581
Sum8117703
Variance1192.0988
MonotonicityNot monotonic
2022-12-28T13:54:03.997564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3615
 
1.8%
18 2924
 
1.5%
15 2912
 
1.5%
21 2841
 
1.4%
19 2824
 
1.4%
10 2756
 
1.4%
7 2753
 
1.4%
25 2752
 
1.4%
9 2752
 
1.4%
24 2752
 
1.4%
Other values (162) 152285
77.1%
(Missing) 16262
 
8.2%
ValueCountFrequency (%)
-4 1
 
< 0.1%
-3 1
 
< 0.1%
-2 13
 
< 0.1%
-1 6
 
< 0.1%
0 3615
1.8%
1 950
 
0.5%
2 1622
0.8%
3 2212
1.1%
4 2624
1.3%
5 2607
1.3%
ValueCountFrequency (%)
171 1
 
< 0.1%
169 1
 
< 0.1%
168 1
 
< 0.1%
165 1
 
< 0.1%
164 1
 
< 0.1%
163 1
 
< 0.1%
162 1
 
< 0.1%
160 9
< 0.1%
159 1
 
< 0.1%
158 19
< 0.1%

total_busy_dashers
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct159
Distinct (%)0.1%
Missing16262
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean41.739747
Minimum-5
Maximum154
Zeros4171
Zeros (%)2.1%
Negative21
Negative (%)< 0.1%
Memory size1.5 MiB
2022-12-28T13:54:04.142557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-5
5-th percentile3
Q115
median34
Q362
95-th percentile105
Maximum154
Range159
Interquartile range (IQR)47

Descriptive statistics

Standard deviation32.145733
Coefficient of variation (CV)0.7701468
Kurtosis-0.19040114
Mean41.739747
Median Absolute Deviation (MAD)22
Skewness0.78246259
Sum7561823
Variance1033.3481
MonotonicityNot monotonic
2022-12-28T13:54:04.286113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4171
 
2.1%
10 3114
 
1.6%
13 3052
 
1.5%
6 3040
 
1.5%
18 3001
 
1.5%
11 2981
 
1.5%
8 2977
 
1.5%
12 2954
 
1.5%
16 2935
 
1.5%
9 2934
 
1.5%
Other values (149) 150007
76.0%
(Missing) 16262
 
8.2%
ValueCountFrequency (%)
-5 1
 
< 0.1%
-4 2
 
< 0.1%
-3 2
 
< 0.1%
-2 3
 
< 0.1%
-1 13
 
< 0.1%
0 4171
2.1%
1 1596
 
0.8%
2 2286
1.2%
3 2644
1.3%
4 2841
1.4%
ValueCountFrequency (%)
154 1
 
< 0.1%
153 1
 
< 0.1%
152 2
 
< 0.1%
150 2
 
< 0.1%
149 1
 
< 0.1%
148 31
< 0.1%
147 7
 
< 0.1%
146 29
< 0.1%
145 36
< 0.1%
144 8
 
< 0.1%

total_outstanding_orders
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct281
Distinct (%)0.2%
Missing16262
Missing (%)8.2%
Infinite0
Infinite (%)0.0%
Mean58.050065
Minimum-6
Maximum285
Zeros4111
Zeros (%)2.1%
Negative44
Negative (%)< 0.1%
Memory size1.5 MiB
2022-12-28T13:54:04.432758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-6
5-th percentile3
Q117
median41
Q385
95-th percentile169
Maximum285
Range291
Interquartile range (IQR)68

Descriptive statistics

Standard deviation52.66183
Coefficient of variation (CV)0.90717953
Kurtosis0.86124431
Mean58.050065
Median Absolute Deviation (MAD)29
Skewness1.1953218
Sum10516698
Variance2773.2684
MonotonicityNot monotonic
2022-12-28T13:54:04.574849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4111
 
2.1%
9 2744
 
1.4%
10 2705
 
1.4%
8 2685
 
1.4%
6 2672
 
1.4%
7 2621
 
1.3%
13 2618
 
1.3%
11 2586
 
1.3%
18 2566
 
1.3%
5 2559
 
1.3%
Other values (271) 153299
77.6%
(Missing) 16262
 
8.2%
ValueCountFrequency (%)
-6 5
 
< 0.1%
-5 6
 
< 0.1%
-4 3
 
< 0.1%
-3 8
 
< 0.1%
-2 6
 
< 0.1%
-1 16
 
< 0.1%
0 4111
2.1%
1 1511
 
0.8%
2 2079
1.1%
3 2334
1.2%
ValueCountFrequency (%)
285 1
 
< 0.1%
283 1
 
< 0.1%
278 20
< 0.1%
277 1
 
< 0.1%
276 35
< 0.1%
274 14
 
< 0.1%
273 1
 
< 0.1%
272 33
< 0.1%
270 19
< 0.1%
269 21
< 0.1%
Distinct98
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean308.56018
Minimum0
Maximum2715
Zeros94
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-12-28T13:54:04.716563image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile251
Q1251
median251
Q3446
95-th percentile446
Maximum2715
Range2715
Interquartile range (IQR)195

Descriptive statistics

Standard deviation90.139653
Coefficient of variation (CV)0.2921299
Kurtosis4.0715872
Mean308.56018
Median Absolute Deviation (MAD)0
Skewness1.1440917
Sum60918419
Variance8125.1571
MonotonicityNot monotonic
2022-12-28T13:54:04.868991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
251 139013
70.4%
446 58221
29.5%
0 94
 
< 0.1%
1487 2
 
< 0.1%
642 2
 
< 0.1%
447 2
 
< 0.1%
1740 2
 
< 0.1%
521 2
 
< 0.1%
303 1
 
< 0.1%
71 1
 
< 0.1%
Other values (88) 88
 
< 0.1%
ValueCountFrequency (%)
0 94
< 0.1%
3 1
 
< 0.1%
10 1
 
< 0.1%
12 1
 
< 0.1%
15 1
 
< 0.1%
20 1
 
< 0.1%
32 1
 
< 0.1%
39 1
 
< 0.1%
53 1
 
< 0.1%
54 1
 
< 0.1%
ValueCountFrequency (%)
2715 1
< 0.1%
1740 2
< 0.1%
1676 1
< 0.1%
1673 1
< 0.1%
1642 1
< 0.1%
1623 1
< 0.1%
1553 1
< 0.1%
1487 2
< 0.1%
1431 1
< 0.1%
1310 1
< 0.1%
Distinct1336
Distinct (%)0.7%
Missing526
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean545.35893
Minimum0
Maximum2088
Zeros9
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2022-12-28T13:54:05.003281image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile192
Q1382
median544
Q3702
95-th percentile909
Maximum2088
Range2088
Interquartile range (IQR)320

Descriptive statistics

Standard deviation219.3529
Coefficient of variation (CV)0.40221749
Kurtosis-0.40508768
Mean545.35893
Median Absolute Deviation (MAD)160
Skewness0.1477314
Sum1.0738226 × 108
Variance48115.696
MonotonicityNot monotonic
2022-12-28T13:54:05.139315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
549 386
 
0.2%
512 385
 
0.2%
590 375
 
0.2%
603 373
 
0.2%
563 368
 
0.2%
556 367
 
0.2%
560 367
 
0.2%
595 362
 
0.2%
547 360
 
0.2%
568 360
 
0.2%
Other values (1326) 193199
97.9%
(Missing) 526
 
0.3%
ValueCountFrequency (%)
0 9
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 6
< 0.1%
4 3
 
< 0.1%
5 2
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
ValueCountFrequency (%)
2088 1
< 0.1%
1739 1
< 0.1%
1711 1
< 0.1%
1680 1
< 0.1%
1667 1
< 0.1%
1547 1
< 0.1%
1525 1
< 0.1%
1460 1
< 0.1%
1453 1
< 0.1%
1448 2
< 0.1%

Interactions

2022-12-28T13:53:56.854997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:26.888072image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:29.415314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:31.845378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:34.216362image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:36.688158image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:39.113267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:41.603205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:44.315646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:46.776163image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:49.277712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:52.003497image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:54.457626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:57.035045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:27.086622image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:29.590250image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:32.015990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:34.394846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:36.855968image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:39.299851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:41.783555image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:44.515505image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:46.954577image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:49.485093image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:52.173139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:54.654477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:57.227329image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:27.257997image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:29.776317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:32.194306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:34.585299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:37.038511image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:39.565387image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:42.175476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:44.715061image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:47.144515image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:49.675967image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:52.346453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:54.855635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:57.407629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:27.437596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:29.963639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:32.365267image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:34.928875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:37.213661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:39.753302image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:42.355433image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:44.904793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:47.320660image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:49.867366image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:52.526137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:55.043203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:57.587698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:27.608317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:30.136149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:32.534557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:35.102462image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:37.375237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:39.932378image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:42.532474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:45.102315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:47.497786image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:50.055652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:52.712534image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:55.229917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:57.797175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:27.806801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:30.324142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:32.724514image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:35.293592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:37.543305image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:40.112693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:42.727564image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:45.298733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:47.688650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:50.244411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:52.905443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:55.407007image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:57.993933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:27.984167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:30.524806image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:32.916760image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:35.484929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:37.749765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:40.294544image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:42.935298image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:45.495813image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:47.895671image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:50.434535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:53.102875image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:55.587656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:58.196646image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:28.163454image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:30.757755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:33.105900image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:35.663255image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:37.947176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:40.486405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:43.133118image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:45.675488image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:48.093203image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:50.887693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:53.305095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:55.764130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:58.407708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:28.339906image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:30.929723image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:33.278921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:35.837757image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:38.144652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:40.675148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:43.329930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:45.859067image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:48.303592image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:51.074603image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:53.503525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:55.946427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:58.613581image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:28.525918image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:31.116921image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:33.468232image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:36.003664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:38.348618image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:40.863288image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:43.523271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:46.043435image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:48.505866image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:51.267868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:53.694727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:56.133545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:58.808748image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:28.730422image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:31.306777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:33.646045image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:36.178441image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:38.549135image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:41.055098image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:43.726939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:46.233211image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:48.698475image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:51.456192image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:53.896732image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:56.318189image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:59.002750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:28.907868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:31.485885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:33.839637image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:36.339411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:38.742635image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:41.235286image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:43.912376image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:46.412521image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:48.887733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:51.645101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:54.074678image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:56.490696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:59.205043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:29.240404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:31.673377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:34.015650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:36.512538image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:38.927675image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:41.416265image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:44.109467image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:46.597221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:49.075664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:51.824629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:54.266859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-12-28T13:53:56.674857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-12-28T13:54:05.269513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
market_idstore_idorder_protocoltotal_itemssubtotalnum_distinct_itemsmin_item_pricemax_item_pricetotal_onshift_dasherstotal_busy_dasherstotal_outstanding_ordersestimated_order_place_durationestimated_store_to_consumer_driving_durationstore_primary_category
market_id1.0000.023-0.012-0.010-0.016-0.000-0.0020.0020.1100.0800.078-0.0610.0090.177
store_id0.0231.0000.018-0.009-0.015-0.0100.001-0.017-0.020-0.020-0.0190.0170.0010.170
order_protocol-0.0120.0181.000-0.010-0.073-0.025-0.045-0.0970.1610.1640.154-0.708-0.0080.319
total_items-0.010-0.009-0.0101.0000.6640.917-0.591-0.0070.0570.0530.059-0.0010.0180.021
subtotal-0.016-0.015-0.0730.6641.0000.6480.0270.5920.1640.1600.1690.0630.0410.090
num_distinct_items-0.000-0.010-0.0250.9170.6481.000-0.5660.0790.0680.0640.0710.0110.0210.078
min_item_price-0.0020.001-0.045-0.5910.027-0.5661.0000.4300.0550.0560.0570.0560.0030.106
max_item_price0.002-0.017-0.097-0.0070.5920.0790.4301.0000.1680.1650.1730.0870.0240.158
total_onshift_dashers0.110-0.0200.1610.0570.1640.0680.0550.1681.0000.9660.957-0.2100.0520.099
total_busy_dashers0.080-0.0200.1640.0530.1600.0640.0560.1650.9661.0000.962-0.2120.0500.096
total_outstanding_orders0.078-0.0190.1540.0590.1690.0710.0570.1730.9570.9621.000-0.2030.0490.091
estimated_order_place_duration-0.0610.017-0.708-0.0010.0630.0110.0560.087-0.210-0.212-0.2031.000-0.0240.100
estimated_store_to_consumer_driving_duration0.0090.001-0.0080.0180.0410.0210.0030.0240.0520.0500.049-0.0241.0000.038
store_primary_category0.1770.1700.3190.0210.0900.0780.1060.1580.0990.0960.0910.1000.0381.000

Missing values

2022-12-28T13:53:59.488827image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-28T13:53:59.936274image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-28T13:54:00.745402image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

market_idcreated_atactual_delivery_timestore_idstore_primary_categoryorder_protocoltotal_itemssubtotalnum_distinct_itemsmin_item_pricemax_item_pricetotal_onshift_dasherstotal_busy_dasherstotal_outstanding_ordersestimated_order_place_durationestimated_store_to_consumer_driving_duration
01.02015-02-06 22:24:172015-02-06 23:27:161845american1.0434414557123933.014.021.0446861.0
12.02015-02-10 21:49:252015-02-10 22:56:295477mexican2.0119001140014001.02.02.0446690.0
23.02015-01-22 20:39:282015-01-22 21:09:095477NaN1.0119001190019001.00.00.0446690.0
33.02015-02-03 21:21:452015-02-03 22:13:005477NaN1.066900560018001.01.02.0446289.0
43.02015-02-15 02:40:362015-02-15 03:20:265477NaN1.0339003110016006.06.09.0446650.0
53.02015-01-28 20:30:382015-01-28 21:08:585477NaN1.0350003150019002.02.02.0446338.0
63.02015-01-31 02:16:362015-01-31 02:43:005477NaN1.02390021200270010.09.09.0446638.0
73.02015-02-12 03:03:352015-02-12 03:36:205477NaN1.044850475018007.08.07.0446626.0
82.02015-02-16 00:11:352015-02-16 00:38:015477indian3.044771382016048.06.018.0446289.0
93.02015-02-18 01:15:452015-02-18 02:08:575477NaN1.022100270012002.02.02.0446715.0
market_idcreated_atactual_delivery_timestore_idstore_primary_categoryorder_protocoltotal_itemssubtotalnum_distinct_itemsmin_item_pricemax_item_pricetotal_onshift_dasherstotal_busy_dasherstotal_outstanding_ordersestimated_order_place_durationestimated_store_to_consumer_driving_duration
1974181.02015-01-30 20:50:232015-01-30 22:24:382956fast4.021528263972926.029.034.0251791.0
1974191.02015-01-26 21:25:122015-01-26 22:21:222956fast4.01809172972920.018.018.0251818.0
1974202.02015-02-18 02:16:042015-02-18 03:12:362956indian3.032352349968549.049.065.0251560.0
1974211.02015-01-30 03:35:012015-01-30 04:42:192956fast4.029792145339NaNNaNNaN251614.0
1974221.02015-01-31 19:48:152015-01-31 20:27:392956fast4.072445314558523.024.024.0251608.0
1974231.02015-02-17 00:19:412015-02-17 01:24:482956fast4.031389334564917.017.023.0251331.0
1974241.02015-02-13 00:01:592015-02-13 00:58:222956fast4.063010440582512.011.014.0251915.0
1974251.02015-01-24 04:46:082015-01-24 05:36:162956fast4.051836330039939.041.040.0251795.0
1974261.02015-02-01 18:18:152015-02-01 19:23:223630sandwich1.01117515355357.07.012.0446384.0
1974271.02015-02-08 19:24:332015-02-08 20:01:413630sandwich1.042605442575020.020.023.0446134.0